452 
Fishery Bulletin 1 10(4) 
Ogburn et al., 2012). Therefore, estimates of larval 
supply could be a useful tool for fisheries managers; 
however, to aid management, monitoring must be cost 
effective and time efficient even as it is conducted in 
ways that minimize sampling error. The objectives of 
this study were to determine the variation in estimates 
of annual larval supply at sampling intervals of 1, 2, 3, 
4, 5, and 7 days and to compare the utility of estimates 
of annual larval supply derived from these different 
sampling intervals for predicting fishery landings. 
Materials and methods 
Daily larval supply was determined through the collec- 
tion of nightly megalopal settlement data at the dock of 
the Duke University Marine Laboratory in the Newport 
River estuary, North Carolina. Megalopae were sampled 
nightly with 3 replicate “hog’s hair” settlement collec- 
tors during the period from September to November in 
each of 8 years from 1993 to 2003; data were not col- 
lected in 1997, 1999, or 2001 (Forward et al., 2004). In 
2004-06, the other 3 years in our 11-year record, data 
were collected from June to November (Ogburn et al., 
2009). Collectors were rinsed in freshwater to remove 
megalopae according to standard protocols (Metcalf et 
al., 1995), and megalopae were identified to genus by 
following Ogburn et al. (2011). Some megalopae may 
have been the lesser blue crab ( Callinectes similis), but 
these megalopae likely made up <5% of the total number 
of megalopae collected (Ogburn et al., 2012). Detailed 
descriptions of the methods that we used in this study 
can be found in Forward et al. (2004) and Ogburn et al. 
(2009). Before analysis, gaps in settlement time series 
were filled by using linear interpolation. Differences in 
the variability of daily larval supply among years were 
explored through calculation of the mean number of 
megalopae collected in each hog’s hair collector for each 
day (megalopae collector -1 day -1 ), standard error of the 
mean, and index of dispersion (ID=variance/mean). 
To generate estimates of annual larval supply at dif- 
ferent sampling intervals, the daily supply data were 
subsampled at intervals of 2, 3, 4, 5, and 7 days because 
these intervals were the simulated sampling intervals 
used in Hettler et al. (1997). Subsampled time series 
were generated beginning with each possible start date 
such that there were 2 subsampled data sets at a 2 -day 
interval beginning on either day 1 or day 2 of the origi- 
nal daily data, 3 data sets at a 3-day interval beginning 
on day 1, day 2, or day 3 of the original daily data, and 
so on. Mean daily settlement was used as the proxy for 
annual larval supply. For comparisons of interannual 
variability and sampling interval, we calculated the 
coefficient of variation (CV) of the annual means for the 
11 years of data for each sampling interval. 
Observed and subsampled estimates of annual larval 
supply were compared with data on fishery landings 
with correlation analysis. Data on fishery landings (in 
kilograms) and effort (catch per trip) were obtained 
from the North Carolina Division of Marine Fisheries 
for hard, soft, and peeler crabs landed statewide in the 
crab pot fishery. Catch per unit of effort (CPUE) was 
compared with mean annual larval supply derived from 
daily sampling at a lag of 2 years, the approximate age 
at which crabs enter the fishery (Forward et al., 2004). 
Confidence intervals were calculated as in Wing et al. 
(1995). CPUE data were not available for comparison 
with 2005 and 2006 larval supply at the time of this 
analysis. One outlier (1994 larval supply and 1996 land- 
ings) was removed before analysis because of extremely 
high CPUE relative to larval supply. The remaining 
estimates of mean annual larval supply were signifi- 
cantly correlated with CPUE (coefficient of correlation 
[r] = 0.88, P=0.003). These estimates represent a useful 
sample data set derived from observed data for testing 
the effect of sampling interval on recruit-stock relation- 
ships. Please note that this analysis is not intended to 
be a recruit-stock analysis for the blue crab fishery in 
North Carolina. For such an analysis, see Ogburn et 
al. (2012). 
Subsampling the data on daily larval supply gener- 
ated more than one estimate of supply for each year, 
yielding many possible combinations of annual esti- 
mates. For example, there were 2 possible abundance 
estimates for each year at a 2-day sampling interval, 
depending on the start date of sampling (e.g., sampling 
start date of 1 September or 2 September). With 2 pos- 
sible values for each of the 8 years of data, there were 
2 8 or 256 possible data combinations. The number of 
possible combinations increased dramatically with sam- 
pling interval (Table 1). For the subsampled estimates 
of larval supply, significant correlations between lagged 
supply and CPUE were considered to be those corre- 
lations that exceeded the 95% confidence interval of 
the comparison between data from daily sampling and 
CPUE (r>0.69). Analyses were conducted in ActiveState 
Perl, vers. 5.10.0 (The Perl Foundation, Walnut Creek, 
CA 1 ). 
Results 
The variability of estimates of annual larval supply 
was higher in years dominated by a single large pulse 
in larval supply and increased as sampling interval 
increased. For the sample period of September to Novem- 
ber, both the ID (calculated from daily data; Fig. 1) 
and the variation in estimates of annual larval supply 
at the 2-day sampling interval (Fig. 2) were lowest in 
1998 ( I D = 34 ; 10.8-11.1 megalopae collector -1 day -1 ) 
and highest at the 7-day interval in 1996 (ID=2027; 
29-167 megalopae collector -1 day -1 ). The mean CV of 
the 7-day sampling interval was more than 2.5 times 
the mean CV of the 2-day interval (Table 2). Extending 
the sampling period to June-November (2004-06 only) 
1 Mention of trade names is for identification purposes only 
and does not imply endorsement by the National Marine 
Fisheries Service, NOAA. 
